code: def inception(kvals):
      (p1, p2, p3, p4) = kvals
  
      branch1 = [Conv((1, 1, p1[0]), **common)]
      branch2 = [
          Conv((1, 1, p2[0]), **common),
          Conv((3, 3, p2[1]), **commonp1),
      ]
      branch3 = [
          Conv((1, 1, p3[0]), **common),
          Conv((5, 5, p3[1]), **commonp2),
      ]
      branch4 = [
          Pooling(op="max", **pool3s1p1),
          Conv((1, 1, p4[0]), **common),
      ]
      return MergeBroadcast(
          layers=[branch1, branch2, branch3, branch4], merge="depth"
      )
  
file_path: neon/examples/convnet-benchmarks/googlenet_v1.py
imports: from neon import NervanaObject
  from neon.util.argparser import NeonArgparser
  from neon.initializers import Xavier
  from neon.layers import Conv, Pooling, GeneralizedCost, Affine, MergeBroadcast
  from neon.optimizers import GradientDescentMomentum, MultiOptimizer, Schedule
  from neon.transforms import Rectlin, CrossEntropyMulti
  from neon.models import Model
  from neon.data import ArrayIterator
  import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: inception(kvals) and returns: MergeBroadcast